Carbohydrate Glycemic Index Research Articles

Carbohydrate quality index and risk of non-alcoholic fatty liver disease in Iranian adults

Background/aimIn the current study, we aimed to assess the association of carbohydrate quality index (CQI) with the risk of non-alcoholic fatty liver disease (NAFLD) in Iranian adults.MethodsThis case-control study was conducted on 225 newly diagnosed NAFLD patients and 450 controls, aged 20–60 years. A food frequency questionnaire was used to calculate the CQI and its components, including fiber intake, glycemic index, whole grains: total grains ratio, and solid carbohydrates: total carbohydrates ratio. Multivariable logistic regression was used to estimate the odds ratio (OR) of NAFLD across the tertile of CQI and its components.ResultsThe participant’s mean ± SD of body mass index and age were 26.8 ± 4.3 kg/m2 and 38.1 ± 8.8 years, respectively. The median (interquartile) CQI score in participants of the case and control groups was 20 (15–25) and 23 (18–28), respectively. In the multivariable-adjusted model, the risk of NAFLD decreased significantly across the tertiles of the CQI [(OR: 0.20; %95CI: 0.11–0.39), Ptrend <0.001)]. Also, the odds of NAFLD decreased across tertiles of solid carbohydrates to total carbohydrates ratio [(OR: 0.39; 95%CI: 0.22–0.69), Ptrend <0.001)]. However, a high dietary glycemic index (GI) was associated with increased odds of NAFLD [(OR:7.47; 95%CI: 3.89–14.33, Ptrend<0.001)]. There was no significant relationship between other CQI components, including fiber intake and whole grain/total grains and the risk of NAFLD.ConclusionsOur results revealed that a diet with a high quality of carbohydrates, characterized by higher intakes of solid carbohydrates, whole grain, and low GI carbohydrates, can be related to a reduced risk of NAFLD.

The Effect of Meal Frequency and Glycemic Index During the Night Shift on Alertness, Hunger, and Gastrointestinal Complaints in Female Health Care Workers—A Two-Armed Randomized Crossover Trial

BackgroundNutrition strategies for night-shift workers could optimize alertness and minimize hunger and reduce gastrointestinal complaints, enhancing safety and well-being. ObjectivesThis study aimed to investigate the effects of 1 or 3 small meals, with either low or high glycemic index (GI), compared with no meal, on alertness, hunger, and gastrointestinal complaints during the night shift. MethodsFifty-one female health care workers, aged 18 to 61 y, participated in a 2-armed randomized crossover design. In 1 study arm, participants received 1 yogurt meal during the night shift, AND in the other, they received 3. Each study arm involved 3 intervention periods during night shifts, with participants consuming yogurt with low GI (1LGI or 3LGI) OR high GI (1HGI or 3HGI) carbohydrates, or no meal (0NGI). Objective alertness was assessed using a validated brief psychomotor vigilance task (PVT-B), subjective alertness with the Samn–Perelli scale, and hunger and gastrointestinal complaints through questionnaires. ResultsParticipants in the 1LGI (β: −4.6; 95% CI: 0.0, 9.3) and 3LGI (β: −3.4; 95% CI: 0.0, 6.8) conditions had fewer lapses during the PVT-B than those in the 3HGI condition. No differences were found between meal conditions for median and reciprocal reaction time or subjective alertness. All 4 conditions reported less hunger (β: from −0.6 to −1.2) compared with no meal. The 3LGI condition resulted in more rumbling intestines than the 3HGI (β: 1.1; 95% CI: 0.4, 1.7) and 0NGI (β: 0.74; 95% CI: 0.11, 1.37) conditions. ConclusionsOur study suggests that consuming 3 small low GI meals during the night shift helps maintain alertness and reduces lapses compared with 3 high GI meals. It also minimizes hunger but may cause mild gastrointestinal complaints.This trial was registered at International Clinical Trial Registry (https://trialsearch.who.int/Trial2.aspx?TrialID%3dNL-OMON25574).

High vs. low glycaemic index pre-exercise feeding

Carbohydrates are proven to be the most important energy source to skeletal muscle during endurance exercise. However, the glucose that carbohydrates provide the muscle with can differ in how quickly they are digested and released into the bloodstream. High glycaemic index (GI) carbohydrates are released into the bloodstream in a rapid burst following digestion whereas low GI carbohydrates are released into the bloodstream at a constant rate after consumption. The effects of low and high GI carbohydrate consumption pre-exercise on endurance exercise performance are not comprehensively understood. Therefore, the aim of this study is to identify the impact of low vs high GI pre-exercise feeding on maximal effort endurance exercise performance. With institutional ethics approval, twelve physically active, healthy males aged 18-40 years attended the laboratory on four separate occasions. On the first occasion, participants underwent a V̇O2max test to identify their maximal aerobic capacity. The final three lab visits consisted of the participants undergoing trials under three differing conditions upon arrival in the fasted state (participants were instructed to not consume food or drink other than water 6 h before arrival). The trial entailed of 20 km cycling followed by 5 km running with 2 min rest in between. Upon arrival, the participants received a carbohydrate beverage, either 40 g high GI, 40 g low GI, or a placebo, 15 min before exercising. Before consumption of the beverage, before the onset of exercise, after each 5 km during the cycle and following the run, heart rate and RPE were recorded, and a finger prick blood sample was taken to analyse for blood glucose and blood lactate concentrations throughout the exercise bout. However, the primary performance indicator was the overall time to complete the trial (bike and run minus 2 min rest). The final order of mean completion time across conditions from fastest to slowest went low GI, high GI, placebo. There was no significant difference between the mean time to completion in the low GI and high GI conditions. However, the low GI condition resulted in a faster time to completion than the placebo condition (P = 0.02) as well as the high GI condition resulting in a faster time to completion than the placebo condition (P = 0.043). Therefore, this could demonstrate that the presence of any carbohydrate type is more influential on endurance exercise performance rather than the glycaemic index of such carbohydrates.

The association between glycemic index, glycemic load and total carbohydrate intake and risk of premature coronary artery disease: Iran Premature Coronary Artery Disease Study (IPAD) - A Case-Control Study

Background and aimsThe association between glycemic index (GI), glycemic load (GL), total carbohydrate intake, and risk of cardiovascular diseases has been controversial. Premature coronary artery disease (PCAD) is characterized by the age of onset lower than 55 and 65 respectively in men and women. The aim of the current study is to investigate the relationship between GI, GL and carbohydrate levels and the risk of PCAD in Iran. Methods and resultsIn total, 419 healthy people and 553 patients struggling with PCAD have participated in this case-control study. Dietary GI and GL were calculated using a validated food frequency questionnaire at the baseline. Crude and multivariable logistic regression were used to assess the relationship between GI, GL, and total carbohydrate intake and risk of PCAD. The mean age of participants was 51.13 ± 6.90 and 46 % of them were women. A significant direct relationship was observed between higher carbohydrate intake (OR: 1.74, 95%CI: 1.27–2.38) and GL levels (OR: 1.56, 95 % CI:1.14–2.14) and risk of PCAD. These associations were not significant after adjusting for potential variables. No significant association has been observed between GI and odds of PCAD even after controlling for all covariates. ConclusionWe found no significant association between GI, GL, and total carbohydrate intake and risk of premature coronary heart disease. Further observational and clinical trials are required to assess this relationship.

Abstract MP09: Diets Varying in Glycemic Index and Carbohydrates With Meal-Timing Specific 12-hour Postprandial Metabolic Changes: The Omnicarb Trial

Background: Both the glycemic index (GI) and amounts of carbohydrates are important determinants of postprandial glucose changes. Recent studies suggest the effects of carbohydrate intake on postprandial glucose metabolism may differ according to meal timing, showing circadian patterns. Hypothesis: We investigated whether the relations of altering both GI levels and the carbohydrate amount with postprandial response of glucose, insulin, and c-peptide differed by meal timing (breakfast, lunch, and dinner), in overweight and obese adults. Methods: The OmniCarb trial was a randomized trial with a crossover design in which participants were given 4 diets varying in GI levels and carbohydrate content. We included 61 participants attended the 12-h postprandial study and completed at least two diets. Area under the curve increment (AUCi) of glucose, insulin, and c-peptide after breakfast (180 min), lunch (240 min), and dinner (120 min), was calculated as outcome. Results: After consuming each diet for 5 weeks, we observed that the diet replacing effects on the postprandial responses in glucose, insulin and c-peptide significantly differed by meal timing. For postprandial glucose response to breakfast, the beneficial effects (lowered AUCi) of replacing high- by low-GI in high-carb diet, replacing high- by low-carb in high-GI diet, and replacing high-GI/high-carb by low-GI/low-carb diet were consistently observed. Such beneficial effects were less significant for lunch and dinner (Figure). For the response of insulin, the beneficial effects of the corresponding diet replacements were more consistently observed in dinner, followed by breakfast. For the response of C-peptide, similar beneficial effects of the corresponding diet replacements were observed for breakfast and dinner, but not lunch. Conclusion: Our results indicate that the beneficial effects of replacing high-GI and/or high-carbohydrate diets by low-GI and/or low-carbohydrate diets on postprandial metabolic changes may differ according to meal timing.

Improved Glycemic Control and Variability: Application of Healthy Ingredients in Asian Staples.

A reduction in carbohydrate intake and low-carbohydrate diets are often advocated to prevent and manage diabetes. However, limiting or eliminating carbohydrates may not be a long-term sustainable and maintainable approach for everyone. Alternatively, diet strategies to modulate glycemia can focus on the glycemic index (GI) of foods and glycemic load (GL) of meals. To assess the effect of a reduction in glycemic load of a 24 h diet by incorporating innovative functional ingredients (β-glucan, isomaltulose) and alternative low GI Asian staples (noodles, rice)on glycemic control and variability, twelve Chinese men (Age: 27.0 ± 5.1 years; BMI:21.6 ± 1.8kg/m2) followed two isocaloric, typically Asian, 24h diets with either a reduced glycemic load (LGL) or high glycemic load (HGL) in a randomized, single-blind, controlled, cross-over design. Test meals included breakfast, lunch, snack and dinner and the daily GL was reduced by 37% in the LGL diet. Continuous glucose monitoring provided 24 h glycemic excursion and variability parameters: incremental area under the curve (iAUC), max glucose concentration (Max), max glucose range, glucose standard deviation (SD), and mean amplitude of glycemic excursion (MAGE), time in range (TIR). Over 24h, the LGL diet resulted in a decrease in glucose Max (8.12 vs. 6.90 mmol/L; p = 0.0024), glucose range (3.78 vs. 2.21 mmol/L; p = 0.0005), glucose SD (0.78 vs. 0.43 mmol/L; p = 0.0002), mean amplitude of glycemic excursion (2.109 vs. 1.008; p < 0.0001), and increase in 4.5–6.5mmol/L TIR (82.2 vs. 94.6%; p = 0.009), compared to the HGL diet. The glucose iAUC, MAX, range and SD improved during the 2 h post-prandial window of each LGL meal, and this effect was more pronounced later in the day. The current results validate the dietary strategy of incorporating innovative functional ingredients (β-glucan, isomaltulose) and replacing Asian staples with alternative low GI carbohydrate sources to reduce daily glycemic load to improve glycemic control and variability as a viable alternative to the reduction in carbohydrate intake alone. These observations provide substantial public health support to encourage the consumption of staples of low GI/GL to reduce glucose levels and glycemic variability. Furthermore, there is growing evidence that the role of chrononutrition, as reported in this paper, requires further examination and should be considered as an important addition to the understanding of glucose homeostasis variation throughout the day.

Dietary Carbohydrate Quality and Quantity and Risk of Breast Cancer among Iranian Women

Objective This study investigated the association between quality and quantity of carbohydrate by assessing low carbohydrates diet score (LCDS), carbohydrate quality score (CQI), glycemic index (GI), dietary glycemic load (GL), and dietary carbohydrate intake, and risk of breast cancer (BrCa) among Iranian women. Methods This hospital-based case-control study was carried out in the Cancer Research Center of Imam Khomeini hospital, Iran. We included One hundred and fifty newly diagnosed BrCa cases and one hundred and fifty healthy controls in this study. Socio-demographic and dietary data and anthropometric measures were recorded. Results We found that a higher CQI than a lower score was associated with a decrease in odds of BrCa (P = 0.04). After adjusting for potential confounders, we observed that CQI was not associated with BrCa development (P = 0.05). An increase in odds of BrCa among women in the highest tertiles of GL (P = 0.12), GI (P = 0.48), and dietary carbohydrate intake (P = 0.06) was seen, which was not statistically significant. There was also a non-significant lower chance of having BrCa with adherence to the LCDS (P = 0.09). Conclusion Our findings suggest that CQI was not related to BrCa risk among Iranian women. This relation deserves to be investigated in prospective studies.

Abstract GS2-07: Glycemic index, glycemic load and breast cancer risk: Results from the prospective NutriNet-Santé cohort

Abstract Background: Evidence is accumulating that high dietary glycemic index (GI) and glycemic load (GL) are risk factors for several metabolic disorders, e.g. diabetes or cardiovascular diseases, but evidence on GI/GL and cancer is less consistent. However, mechanistic data suggest that food with high-GI may contribute to carcinogenesis through the insulin resistance pathway.Objective: The objective was to study the associations between dietary GI/GL and breast cancer risk.Design: Population based prospective cohort study.Setting and Participants: 81,526 women aged ≥18y (mean age: 40.8±14.0y), free of cancer at baseline and with no prevalence or incidence of diabetes, from the French NutriNet-Santé cohort (2009-2019) were included in the analyses. 24-hour dietary records linked with a nutritional composition table for &amp;gt;3,500 food/beverage items containing GI values and carbohydrate content enabled us to compute average dietary GI and GL.Main outcome measures: Associations between GI; GL; proportion of energy and proportion of carbohydrates from low-GI (GI≤55) and from medium/high-GI (GI&amp;gt;55) foods and risk of breast cancer were assessed by Cox proportional hazard models adjusted for known risk factors (sociodemographic, anthropometric, lifestyle, medical history, and nutritional factors).Results: A higher dietary GL was associated with higer postmenopausal breast cancer risk (n=572, hazard ratio for quintile 5 versus quintile 1: 1.64 confidence interval 1.06-2.55 Ptrend=0.03). Percentage of energy intake from medium/high-GI foods was associated with higher breast cancer risk (HRQ5vs.Q1=1.34 (1.05-1.71), Ptrend=0.04), more specifically postmenopausal breast cancer (HRQ5vs.Q1=1.36 (1.00-1.84), Ptrend=0.02). Carbohydrates intake from medium/high-GI foods was also associated with higher breast cancer risk (HRQ5vs.Q1=1.34 (1.05-1.71), Ptrend=0.04) in both premenopausal (HRQ5vs.Q1=1.49 (1.03-2.17), Ptrend=0.04) and postmenopausal (HRQ5vs.Q1=1.44 (1.08-1.93), Ptrend=0.03) women, whereas carbohydrates from low-GI foods was associated with lower breast cancer risk (HRQ5vs.Q1=0.74 (0.59-0.92), Ptrend=0.04), only among postmenopausal women (HRQ5vs.Q1=0.69 (0.52-0.92), Ptrend=0.03). Conclusion: The consumption of foods with high-GI was associated increased breast cancer risk. If these results are confirmed dietary GI and GL should be considered as modifiable risk factor for primary breast cancer prevention. Citation Format: Charlotte Debras, Eloi Chazelas, Bernard Srour, Chantal Julia, Emmanuelle Kesse-Guyot, Laurent Zelek, Cédric Agaësse, Nathalie Druesne-Pecollo, Pilar Galan, Serge Hercberg, Paule Latino-Martel, Mélanie Deschasaux, Mathilde Touvier. Glycemic index, glycemic load and breast cancer risk: Results from the prospective NutriNet-Santé cohort [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr GS2-07.

Glycemic Index of lentil- and cherry-based sport nutrition products for endurance athletes

Summary Objective Endurance athletes may benefit from consuming foods low in glycemic index (GI) before sport performance. Foods such as lentils and tart cherries have a very low GI, but processing these ingredients into products that can easily be consumed before performance may affect their GI. The study determined whether processing lentils into a sports nutrition bar and tart cherries into a drink affected the glycemic index. Methods Using a randomized counterbalanced design, we compared the GI of a lentil-based sports nutrition bar (n = 10) to two versions of a popular sports nutrition bar (i.e. Power Bar Harvest and Power Bar Recovery, n = 9) in young, healthy individuals. We then evaluated the GI of raw tart cherries and juice from tart cherries in another group of young healthy individuals (n = 7). Results The lentil-based sports nutrition bar had a lower GI than the other bars (i.e. 45 ± 17 versus 55 ± 13 and 55 ± 14 for the lentil bar, Power Bar Harvest and Power Bar Recovery, respectively; p 0.05). Conclusions A sports nutrition bar with lentils as a main ingredient or cherry juice offer low GI carbohydrate options for endurance athletes.

Glycemic index, glycemic load, and risk of coronary heart disease: a pan-European cohort study

High carbohydrate intake raises blood triglycerides, glucose, and insulin; reduces HDLs; and may increase risk of coronary heart disease (CHD). Epidemiological studies indicate that high dietary glycemic index (GI) and glycemic load (GL) are associated with increased CHD risk. The aim of this study was to determine whether dietary GI, GL, and available carbohydrates are associated with CHD risk in both sexes. This large prospective study-the European Prospective Investigation into Cancer and Nutrition-consisted of 338,325 participants who completed a dietary questionnaire. HRs with 95% CIs for a CHD event, in relation to intake of GI, GL, and carbohydrates, were estimated using covariate-adjusted Cox proportional hazard models. After 12.8 y (median), 6378 participants had experienced a CHD event. High GL was associated with greater CHD risk [HR 1.16 (95% CI: 1.02, 1.31) highest vs. lowest quintile, p-trend 0.035; HR 1.18 (95% CI: 1.07, 1.29) per 50 g/day of GL intake]. The association between GL and CHD risk was evident in subjects with BMI (in kg/m2) ≥25 [HR: 1.22 (95% CI: 1.11, 1.35) per 50 g/d] but not in those with BMI <25 [HR: 1.09 (95% CI: 0.98, 1.22) per 50 g/d) (P-interaction=0.022). The GL-CHD association did not differ between men [HR: 1.19 (95% CI: 1.08, 1.30) per 50 g/d] and women [HR: 1.22 (95% CI: 1.07, 1.40) per 50 g/d] (test for interaction not significant). GI was associated with CHD risk only in the continuous model [HR: 1.04 (95% CI: 1.00, 1.08) per 5 units/d]. High available carbohydrate was associated with greater CHD risk [HR: 1.11 (95% CI: 1.03, 1.18) per 50 g/d]. High sugar intake was associated with greater CHD risk [HR: 1.09 (95% CI: 1.02, 1.17) per 50 g/d]. This large pan-European study provides robust additional support for the hypothesis that a diet that induces a high glucose response is associated with greater CHD risk.

Effects of carbohydrate quality and amount on plasma lactate: results from the OmniCarb trial

IntroductionPlasma lactate is a marker of non-oxidative glucose metabolism associated with progression to diabetes. We examined the effect of carbohydrate quality (glycemic index (GI)) and amount (%kcal) on plasma lactate....

Correlation between High Carbohydrate Foods with Glycemic Index

High carbohydrate food has been perceived as a food with high glycemic index (GI). Meanwhile, the risks of diabetes are frequently associated with the GI carbohydrate based foods. Therefore, a comprehensive study based on the literature review regarding the relationship between high-carbohydrate food and the glycemic index needs to be conducted. High-carbohydrate foods can be grouped into the available carbohydrates type and non-available carbohydrates type. Food with available carbohydrates such as glucose, disaccharide, digestible oligosaccharides, and starch have positive correlation with the GI. The non-available forms of carbohydrates are hardly digested by the body, so they usually have low GI. The non-available carbohydrates foods are fructooligosaccharide (FOS) and galactooligosaccharide (GOS), raffinose, stachyose, and verbascose. High-carbohydrate foods can have low GI value due to complex carbohydrates or resistant starches. The type of carbohydrate can be turned into non-available due to chemical modification, processing, or interacting with other components. This information is necessary because recently, people have high awareness in choosing carbohydrate food. Not only the amount consumed, but also its carbohydrate content, types of carbohydrates, and how they are processed are important to be observed.

Effects of Pre-Exercise High and Low Glycaemic Meal on Intermittent Sprint and Endurance Exercise Performance.

The purpose of this study is to investigate the effects of ingesting either a high glycaemic index (HGI) or low glycaemic index (LGI) carbohydrate meal (preceding a 12 h overnight fast and where the meal was ingested 45-min prior to activity) on intermittent sprint and endurance exercise performance. Ten male varsity athletes from intermittent sports (age 23.6 ± 1.7 years, VO2max 51.9 ± 4.7 mL·kg−1·min−1) underwent a peak velocity (Vpeak) test and familiarisation session, followed by two experimental sessions in random order. Experimental sessions involved the ingestion of either an HGI or LGI meal, followed by the completion of the modified Loughborough Intermittent Shuttle Test (mLIST). There was no significant difference between HGI or LGI meals on sprint times (p = 0.62) and distance to exhaustion (p = 0.54) in the mLIST. Exercise heart rate, blood lactate and ratings of perceived exertion were also similar between the two meal trials throughout the mLIST (all p > 0.05). Subjective ratings of hunger, fullness, satiety and satisfaction were also not significantly different between the two meals. In conclusion, consuming either an HGI or LGI meal after a prolonged 12 h fast and ingesting the meal 45 min prior to exercise did not differ in either physiological, subjective and intermittent sprint and endurance performance outcomes.

A Low Glycemic Index Diet Slows Prostate Cancer Growth (P05-018-19)

ObjectivesCarbohydrates are the main source of energy in older adults in the US. Moreover, they increase insulin and insulin-like growth factor-1 (IGF1), which are implicated in tumor growth by increasing cell survival. Previously, we found low carbohydrate (LC) diets slow prostate cancer (PC) growth and increase survival vs. a Western diet (WD) in mice. However, long-term adherence to a LC diet can be difficult for cancer patients. Thus, we aimed to determine whether modifying carbohydrate quality without changing quantity could result in the same outcome as when quantity is reduced. Carbohydrate quality was based on a glycemic index (GI), which indicates how carbohydrates affect blood glucose levels. Low GI carbohydrates are absorbed more slowly and result in a lower and slower increase in glucose levels and insulin demand than high GI carbohydrates. We hypothesized that high carbohydrate intake but with a low GI would slow PC growth, by reducing insulin levels. MethodsA xenograft mice study compared the effect on PC growth of 3 diets: HiGI WD (48% carbohydrate: sucrose), HiGI LC (20% carbohydrate: sucrose), and LoGI WD (48% carbohydrate: amylose, amylopectin, maltodextrin). Male SCID mice were fed an ad lib HiGI WD. At day 14, they were injected with 5 × 105 LAPC-4 cells. When tumors reached ∼200 mm3, mice were single-housed and randomized to their diets (n = 33/group). Mice were pair-fed to ensure all had the same weight throughout the study. Tumor volume and body weight were measured 2X per week. Body composition was determined by Echo-MRI. The outcomes of the study were tumor volume, survival, glucose, insulin, IGF-1 and IGFBP-3 levels, and tumor tissue analysis. ResultsLoGI WD mice had lower tumor volumes, insulin, IGF1, and IGF1: IGFBP3 ratio, and higher IGF-BP3 levels, than the other two groups. Blood glucose was similar across arms. Even though body weight was similar across arms, LoGI WD mice had lower body fat. In a meal tolerance test, glucose was higher in HiGI WD mice with comparable results between the other two diets. ConclusionsFeeding mice a low GI diet delayed PC growth and decreased serum insulin, IGF1 and IGF1: IGFBP3 ratios vs. a high GI diet. These data suggest carbohydrate quality is important for PC growth. Whether a low-carbohydrate and low GI diet would have additive benefits remains to be tested. Funding SourcesAmerican Institute for Cancer Research.

Salmon in Combination with High Glycemic Index Carbohydrates Increases Diet-Induced Thermogenesis Compared with Salmon with Low Glycemic Index Carbohydrates⁻An Acute Randomized Cross-Over Meal Test Study.

The study investigated the acute effects of meals containing either salmon or veal in combination with carbohydrates with high or low glycemic index (GI) on diet-induced thermogenesis (DIT) (primary endpoint), appetite sensations, and energy intake (EI). Twenty-five overweight men and women ingested four iso-caloric test meals: salmon with mashed potatoes (high GI) (SM), salmon with wholegrain pasta (low GI) (SP), veal with mashed potatoes (VM) and veal with wholegrain pasta (VP). Energy expenditure was measured in the fasting state and six times postprandially for 25 min with 5-min breaks between each measurement. Appetite sensations were measured every 30 min. Blood samples, from arterialized venous blood, were drawn every 20 min until an ad libitum buffet-style lunch was served 3.5 h later. DIT was 40% higher after the SM meal compared to the SP meal (p = 0.002). Prospective food consumption was lower after the SM meal compared with the VP meal (p = 0.01). There were no differences in satiety, hunger, fullness, or ad libitum EI between the test meals (all p > 0.05). In conclusion, salmon with high GI carbohydrates increased DIT compared to salmon with low GI carbohydrates. This indicates that DIT is sensitive to the GI of the carbohydrates after intake of salmon but not veal.

APOE4 Genotype Exerts Greater Benefit in Lowering Plasma Cholesterol and Apolipoprotein B than Wild Type (E3/E3), after Replacement of Dietary Saturated Fats with Low Glycaemic Index Carbohydrates.

We examined the impact of APOE genotype on plasma lipids and glucose in a secondary analysis of data from a five-arm, randomised controlled, parallel dietary intervention trial (‘RISCK’ study), to investigate the impact of replacing saturated fatty acids (SFA) with either monounsaturated fat (MUFA) or carbohydrate of high or low glycaemic index (GI) on CVD risk factors and insulin sensitivity. We tested the impact of APOE genotype (carriage of E2 and E4 alleles versus E3/E3), determined retrospectively, on plasma lipids, lipoproteins and glucose homeostasis at baseline (n = 469), and on the change in these variables after 24 weeks of dietary intervention (n = 389). At baseline, carriers of E2 (n = 70), E4 (n = 125) and E3/E3 (n = 274) expressed marked differences in total plasma cholesterol (TC, p = 0.001), low density lipoprotein cholesterol (LDL-C, p < 0.0001), apolipoprotein B (apo B, p < 0.0001) and total to high density lipoprotein cholesterol ratio (TC:HDL-C, p = 0.002), with plasma concentrations decreasing in the order E4 > E3/E3 > E2. Following intervention, there was evidence of a significant diet x genotype interaction with significantly greater decreases in TC (p = 0.02) and apo B (p = 0.006) among carriers of E4 when SFA was replaced with low GI carbohydrate on a lower fat diet (TC −0.28 mmol/L p = 0.03; apo B −0.1 g/L p = 0.02), and a relative increase in TC (in comparison to E3/E3) when SFA was replaced with MUFA and high GI carbohydrates (TC 0.3 mmol/L, p = 0.03). Among carriers of E2 (compared with E3/E3) there was an increase in triacylglycerol (TAG) when SFA was replaced with MUFA and low GI carbohydrates 0.46 mmol/L p = 0.001). There were no significant interactions between APOE genotype and diet for changes in indices of glucose homeostasis. In conclusion, variations in APOE genotype led to differential effects on the lipid response to the replacement of SFA with MUFA and low GI carbohydrates.

Meals based on cod or veal in combination with high or low glycemic index carbohydrates did not affect diet-induced thermogenesis, appetite sensations, or subsequent energy intake differently

The objective of this study was to investigate the acute effects of meals containing protein from either cod or veal in combination with high or low glycemic index (GI) carbohydrates, on diet-induced thermogenesis (DIT) (primary endpoint), appetite, energy intake (EI), as well as postpranidal ghrelin, glucose, and insulin responses. Twenty-three overweight men and women (mean ± SD age: 30.0 ± 7.6 y, BMI: 27.2 ± 1.4 kg/m2) consumed 4 test meals: cod with mashed potatoes (high GI carbohydrate), cod with wholegrain pasta (low GI carbohydrate), veal with mashed potatoes, and veal with wholegrain pasta (∼2010 kJ, ∼25.5 E% protein, ∼41.0 E% carbohydrate, ∼33.5 E% fat). Energy expenditure was measured at baseline and six times postprandially, each lasting 25 min. Additionally, appetite sensations were measured every half hour. Arterialized venous blood samples were drawn every 20 min until an ad libitum buffet-style lunch was served 3.5 h later. DIT did not differ between test meals (P > 0.05), and there were no differences in appetite sensations or ad libitum EI (all, P > 0.05). Meal-time interactions were found for glucose and insulin (P = 0.04 and P < 0.001). Pairwise comparisons revealed that glucose and insulin peaks were higher after the meals with high GI carbohydrates. No differences were found between meals with cod or veal in combination with carbohydrates with low or high GI on DIT, appetite sensations, or EI in overweight men and women. However, as expected meals with high GI carbohydrates resulted in higher glucose and insulin responses compared to meals with low GI carbohydrates regardless of protein source.

High Dietary Glycemic Load is Associated with Poor Functional Outcome in Patients with Acute Cerebral Infarction.

Background and PurposeElevated postprandial blood glucose is a critical risk factor for stroke. The dietary glycemic load (GL) and glycemic index (GI) are frequently used as markers of the postprandial blood glucose response to estimate the overall glycemic effect of diets. We hypothesized that high dietary GL, GI, or total carbohydrate intake is associated with a poor functional outcome in patients with acute ischemic stroke.MethodsWe prospectively included 263 first-ever ischemic stroke patients who completed a semiquantitative food-frequency questionnaire. The dietary GL, GI, and total carbohydrate intake were investigated by examining the average frequency of intake during the previous year based on reference amounts for various food items. Poor functional outcome was defined as a score on the modified Rankin Scale (mRS) of ≥3 at 3 months after stroke.ResultsThe patients were aged 65.4±11.7 years (mean±standard deviation), and 58.2% of them were male. A multivariate analysis adjusted for age, sex, marital status, prestroke mRS score, diabetes mellitus, hyperlipidemia, body mass index, triglycerides, low-density lipoprotein, hemoglobin A1c, stroke classification, and National Institutes of Health Stroke Scale score, early neurological deterioration, and high-grade white-matter hyperintensities revealed that the dietary GL and total carbohydrate intake were associated with a poor functional outcome, with odds ratios for the top quartile relative to the bottom quartile of 28.93 (95% confidence interval=2.82–296.04) and 36.84 (95% confidence interval=2.99–453.42), respectively (p for trend=0.002 and 0.002, respectively). In contrast, high dietary GI was not associated with a poor functional outcome (p for trend=0.481).ConclusionsIncreased dietary GL and carbohydrate intake were associated with a poor short-term functional outcome after an acute ischemic stroke.

Sex differences in adolescents' glycaemic and insulinaemic responses to high and low glycaemic index breakfasts: a randomised control trial.

During puberty young people undergo significant hormonal changes which affect metabolism and, subsequently, health. Evidence suggests there is a period of transient pubertal insulin resistance, with this effect greater in girls than boys. However, the response to everyday high and low glycaemic index (GI) meals remains unknown. Following ethical approval, forty adolescents consumed a high GI or low GI breakfast, in a randomised cross-over design. Capillary blood samples were taken during a 2-h postprandial period, examining the glycaemic and insulinaemic responses. Maturity offset and homoeostatic model assessment (HOMA) were also calculated. The glycaemic response to the breakfasts was similar between boys and girls, as shown by similar peak blood glucose concentrations and incremental AUC (IAUC) following both high and low GI breakfasts (all P>0·05). Girls exhibited a higher peak plasma insulin concentration 30 min post-breakfast following both high GI (P=0·043, g=0·69) and low GI (P=0·010, g=0·84) breakfasts, as well as a greater IAUC following high GI (P=0·041, g=0·66) and low GI (P=0·041, g=0·66) breakfasts. HOMA was positively correlated with the insulinaemic responses (all P<0·0005) and maturity offset (P=0·037). The findings of the present study suggest that pubertal insulin resistance affects the postprandial insulinaemic responses to both high and low GI meals. Specifically, girls exhibit a greater insulinaemic response than boys to both meals, despite similar glycaemic responses. This study is the first to report the glycaemic and insulinaemic responses to everyday meals in boys and girls, supporting the recommendation for young people to base their diet on low GI carbohydrates.

Algorithms to Improve the Prediction of Postprandial Insulinaemia in Response to Common Foods.

Dietary patterns that induce excessive insulin secretion may contribute to worsening insulin resistance and beta-cell dysfunction. Our aim was to generate mathematical algorithms to improve the prediction of postprandial glycaemia and insulinaemia for foods of known nutrient composition, glycemic index (GI) and glycemic load (GL). We used an expanded database of food insulin index (FII) values generated by testing 1000 kJ portions of 147 common foods relative to a reference food in lean, young, healthy volunteers. Simple and multiple linear regression analyses were applied to validate previously generated equations for predicting insulinaemia, and develop improved predictive models. Large differences in insulinaemic responses within and between food groups were evident. GL, GI and available carbohydrate content were the strongest predictors of the FII, explaining 55%, 51% and 47% of variation respectively. Fat, protein and sugar were significant but relatively weak predictors, accounting for only 31%, 7% and 13% of the variation respectively. Nutritional composition alone explained only 50% of variability. The best algorithm included a measure of glycemic response, sugar and protein content and explained 78% of variation. Knowledge of the GI or glycaemic response to 1000 kJ portions together with nutrient composition therefore provides a good approximation for ranking of foods according to their “insulin demand”.